import torch
import text_preprocess as tp

batch_size = 32
num_steps = 35
train_iter,vocab = tp.load_data_time_machine(batch_size,num_steps)

def get_params(vocab_size,num_hiddens,device):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return torch.randn(size=shape,device=device) * 0.01
    def three():
        return (normal((num_inputs,num_hiddens)),
                normal((num_hiddens,num_hiddens)),
                torch.zeros(num_hiddens,device=device))
    
    W_xz, W_hz, b_z = three()
    W_xr, W_hr, b_r = three()
    W_xh, W_hh, b_h = three()
    W_hq = normal((num_hiddens,num_outputs))
    b_q = torch.zeros(num_outputs,device=device)
    params = [W_xz,W_hz,b_z,W_xr,W_hr,b_r,W_xh,W_hh,b_h,W_hq,b_q]
    for p in params:
        p.requires_grad_(True)
    return params

def init_gru_state(batch_size,num_hiddens,device):
    return (torch.zeros((batch_size,num_hiddens),device=device),)

def gru(inputs,state,params):
    W_xz,W_hz,b_z,W_xr,W_hr,b_r,W_xh,W_hh,b_h,W_hq,b_q = params
    H, = state
    outputs = []
    for X in inputs:
        Z = torch.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)
        R = torch.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)
        H_tilda = torch.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = H @ W_hq + b_q
        outputs.append(Y)
    return torch.cat(outputs,dim=0), (H,)


 
